The Relationship Between NPS Trends and Revenue

The Relationship Between NPS Trends and Revenue

INSIGHTS
The Relationship Between NPS Trends and Revenue
BRIEF

The Relationship Between
NPS Trends and Revenue

Here is the current state of NPS science, along with some pseudo-scientific counterpoints.

Right from the very beginning, right from when Fred Reichheld published his HBR article “The One Number You Need to Know” the subject of the relationship between NPS and revenue has been a controversial one. Some of the debate is sincere. Some of it is simple marketing work created by people who want to promote a different measurement and improvement system.

It is striking that the controversy has been about the number, and not about the validity of the answers to the open text questions that follow the NPS rating. After all, whatever their validity, numbers like NPS, CSAT, Customer Effort and others are not all that important. What is important is what you do to improve.

The price of oil dropped 20% during the period, invalidating their conclusion.

Customer suggestions for improvements are contained in their answers to the ‘Why?’ and ‘What could we do better?’ questions. We have yet to see any debate around them.

Meanwhile, debate about the statistical validity of the numbers is common. It prevents many companies from taking action. Unfortunate, to say the least!

Only overall brand-level NPS and relationship NPS have any value whatsoever as predictors of revenue or market share. There is one possible exception.

There is NPS and NPS

As we have discussed in other articles, the Net Promoter Score is used in a variety of situations, with differing predictive value.

Let us state this clearly: only overall brand-level NPS and relationship NPS values have any value whatsoever as predictors of revenue or market share. Forget at your peril.

For example, unless yours is a pure call center business, the NPS numbers and trends for your support calls have no predictive value whatsoever for your overall business. None.

With one exception. There is what we call ‘Event NPS’. The event in question is the main interaction a customer will have with a business.

In the case of an insurance business, an event might be the end-to-end process for a client to find out how to log a claim, log it, interact with the insurance company, and receive payment.

A more common example is for an e-commerce company.

The process that goes from searching for or learning about a product, to comparing it with others, selecting it for ordering, confirming the order and (in most cases) paying by credit card, is an ordering ‘event’. In e-commerce, the event represents 70% to 80% of the interaction that we have with the company. E-commerce event NPS tends to have good predictive value for market share.

In fact, event NPS is not entirely an exception to the rule about transactions. When a group of sequential transactions (an event) represent a large portion of a customer’s interactions with a company, the NPS results naturally come to resemble brand NPS.

 

Industries vary and improvements are relative

Some market trends cannot be surmounted. If your entire industry is in a 20% annual decline, then you cannot expect that a positive NPS trend will bring sustainable growth.

Remember too that the real importance of an NPS value is not absolute, but relative. If your overall brand NPS improves but your main competitor’s improves more, you will lose market share.

 

Other things matter too

Bain and Satmetrix’s experience lead us to believe that NPS trends explain 20% to 60% of market share trends. While such predictive power is impressive, many other things matter too. For example, your company may launch innovative, impactful products which – you would hope – affect your market share.

Or you may be in an industry where customer satisfaction does not matter much. Retail gas stations are a good example. Their revenue trends are almost exclusively driven by the price of oil. Not much else matters.

 

So … onwards to the current state of research on the topic

Very little academic research is available on the relationship between any satisfaction metric trends and revenue trends. This is not surprising. After all, most double-blind brand-level NPS research is funded by companies that consider the results highly confidential. However, some articles are available.

Andrew Stephenson, Jana Fiserova, Geoff Pugh, and Chris Dimos won a ‘best paper’ award for their study, published in the Annual Proceedings journal of the British Academy of Management. They studied the NPS and revenue performance of individual stores in Britain’s largest retail furniture chain, DFS.

Customers provided feedback six months after purchase – excellent timing for overall brand-level feedback for that type of product. And yes, the stores with better NPS trends outperformed the others by a significant margin.

Do you know what was found in slightly-anonymized versions of what HP found when studying NPS? Yes, there is a relationship between NPS and revenue trends. One important conclusion was that there is a time lag between changes in NPS (compared to competitors) and changes in revenue. The time lag varies depending on customer purchase cycles.

 

Aggressive contradictory views

You know you’re winning when people hate you. OK, agreed, it’s not a sure-fire measure of success. But you do have their attention.

There are individuals and companies who have been somewhat aggressive in pushing contrary views, trying to imply that NPS trends have little or no value. We have seen two main categories of such positions:

  • People who want to promote a compound metric which they have invented
    This is the most common objection we have seen. We accept that compound metrics assembled from responses to three or more questions may have better predictive value than NPS on its own. Neither we nor Fred Reichheld (author of the original HBR article) have said anything else. The objection we have to such indices is that they are much harder than NPS to implement and to communicate. Try starting every discussion about results with a lengthy explanation of how you derived the score. Watch your audience sleep. We strongly recommend avoiding all compound metrics.
  • People whose research is defective or who misrepresent NPS
    It is easy to find the 2006 study by Morgan and Rego about the value of different customer satisfaction and loyalty scores. The authors themselves say that they studied NPS and that a different metric is a better predictor of business performance. Their statement is not entirely truthful. They did not study the “How likely are you to recommend…” question at all! They used a different question as a proxy for it, saying they believe it shows the same thing. This is just one example of misrepresentation. Another peer-reviewed paper covered various industries in Norway. The authors state that NPS trends did not predict revenue over the four-year period, during which three industries were tracked. Retail gas stations were one of the industries. The price of oil dropped 20% during the period, invalidating their conclusion.

 

Conclusion

In addition to the original research by Bain and Satmetrix, there is at least some credible peer-reviewed and other literature supporting the relationship between NPS trends relative to competitors and market share trends. Company confidentiality rules mean it is not surprising that huge volumes of such data and research are not publicly available.

We have not seen any contradictory research that stands up to scrutiny. We do agree that complex, difficult-to-communicate compound metrics with better predictive power may exist.

We say once again that the NPS numbers are not the most important part of a CX measurement and improvement system. What matters are the actions that you take, based on customers’ improvement suggestions. Those actions drive financial results.

ABOUT INSIGHTS FROM OCX COGNITION

OCX Cognition delivers the future of NPS. We ensure customer experience success by combining technology and data science with programmatic consulting. In our Insights section, we present a comprehensive and evolving collection of resources based on our research and expertise, collected for CX leaders committed to delivering business outcomes.

How Much Is a Customer Worth? Good Question

How Much Is a Customer Worth? Good Question

INSIGHTS
How Much Is a Customer Worth? Good Question
BRIEF

How Much Is a Customer Worth?
Good Question

There is currently no accounting standard for customer lifetime value. Here is our proposal.

Financial support for CX initiatives often falls woefully short, compared to other initiatives such as digital transformation. A frequent problem is that the CEO and leadership team treat CX as market research. Report cards are the primary output.

There is a more fundamental issue, though. The customer is at the center of CX. But we have no accounting standard for how much a customer is worth.

A CX team gathers customer feedback. It proposes an initiative that will improve customer retention rates by 2%. How much is that worth? As of now, there is no consistent way to answer that question.

After 15 years of experience with over one thousand NPS implementations, we recognize the lack of answer as a major problem. We now want to propose the solution.

Bear with us as we first expose a relatively traditional approach that works. Then read our radical new solution.

 

Value customers the same way you value companies

When companies and their investment banking partners place a value on a company they want to acquire, there is one single, universally accepted methodology: Discounted Cash Flow. The value of the target company is today’s value of all future cash flows that you expect the company to generate. At a technical level, this is EBITDA cash flow.

The value of your company is the same as the value of your customers, current and future.

Most companies do not track this information at the level of an individual customer

With the exception of B2B businesses that have a very small number of customers, almost no business tracks financial data at the level of the individual customer. Large outsourcing companies are examples of situations where there probably is a P&L for each of their largest customers, at the very least.

So what can you do if you do not have P&L data? One method that you can agree with your CFO is to divide customers into useful segments or groups. Here are some examples:

  • Customers who have made a one-time purchase versus customers who have an ongoing contractual relationship with your company.
  • For businesses that do not have annuity contracts, split customers into groups in terms of RFM. RFM is Recency, Frequency, and Monetary value of their purchases. Customers will be in each of the three groups with different ranks
  • Customers who buy directly from you versus those who buy via resellers, usually providing lower margins.

 

Attaching financial values to the groups

For businesses that have annuity contracts, use past renewal rates as a baseline for future renewal rates. It is important to think of renewal rates in terms of dollars, not units. Customers can renew contracts at a different value to their prior contract.

The data needed to get to something resembling EBITDA by category is below. We are also including data points that are affected by changes in customer happiness, even if they do not enter into traditional P&L accounting.

  • List prices.
  • Discounts or other price reductions that you have granted your customers, and which are not part of a standard published discount table. This is where you give additional discounts to unhappy customers to encourage them to buy more or to renew a contract. These discounts are sometimes referred to as ‘contra revenue’ and they do not show up in standard P&L accounting.
  • Net revenue.
  • Cost of Goods Sold. This cost includes anything you provide to Detractors free of charge, for example. COGS does not include sales and marketing costs. Those are ‘below the line’ costs, not considered in Gross Margin, and are considered in Operating Profit numbers.
  • Cost of attracting a new customer. You will use this to establish the cost of replacing a customer who leaves.

Agree a set of these numbers with your CFO and you will be off to a good start.

Smile. There is an easier, more radical way. We propose it now.

We believe that DCF should be declared the accounting standard for such calculations

The value of your company is the same as the value of your customers, current and future

As we said already, the only correct way to value your company is Discounted Cash Flow. But what is the source of that cash? There is only one source: your customers! Both the ones you have now and the ones you may reasonably expect to gain in the future. What this means is that your customers’ collective lifetime value and your company’s DCF are identical.

To get this value – TCLV – do as follows. Using standard DCF calculations, get the DCF value of a one-point improvement in the collective contract renewal rate for all of your customers together. Call it $X. Confident that you have a CX investment that will improve renewal rates by 0.5%? Multiply $X by 0.005 (which is 0.5%). You have the ROI on that investment.

You can break the figures down. Say your financial data shows that Promoters buy more often or simply more than Passives. Calculate the difference in DCF between the two groups. Use it to justify investments that will turn Passives into Promoters. Repeat with Detractors versus Passives.

To be credible, your justifications should focus on what the Detractors, Passives and Promoters tell you they want you to improve.

 

Conclusion: People have been making TCLV calculations far more complex than necessary

When thought about in terms of company value and the fact that company and customer value are identical, Customer Lifetime Value becomes an easy concept. We believe that DCF should be declared the accounting standard for such calculations. We will do our best to make this happen.

ABOUT INSIGHTS FROM OCX COGNITION

OCX Cognition delivers the future of NPS. We ensure customer experience success by combining technology and data science with programmatic consulting. In our Insights section, we present a comprehensive and evolving collection of resources based on our research and expertise, collected for CX leaders committed to delivering business outcomes.

Calculating the Value of Improving CX in a Product Business

Calculating the Value of Improving CX in a Product Business

INSIGHTS
Calculating the Value of Improving CX in a Product Business
BRIEF

Calculating the Value of Improving
CX in a Product Business

This is an essential step in justifying CX improvement projects

You’re tearing your hair out. It should be screamingly obvious to everyone that the company must invest in CX. What is wrong with people?

But it is sometimes difficult to produce the figures. You’ll need them to justify the company’s investments in projects that improve customer experience.

You’re tearing your hair out. It should be screamingly obvious to everyone that the company must invest in CX. What is wrong with people?

We have a way of simplifying at least part of that discussion, namely the calculation of how much it is worth to improve customer happiness. It is not all that is needed, but it is a very good start.

The method should work for most CX measurement systems and we use NPS in this example.

So relax a bit.

If all of your customers need to phone for help, you probably have deeper issues

What sort of NPS are we talking about?

For the calculation that follows it is critical to know the identity of the customers that we are measuring. We don’t mean their name; any unique identifier will do. This is one of the rare situations where double-blind customer research is not useful.

The reason is that the calculation method requires us to know what Customer X, who provides particular satisfaction ratings, actually does in terms of purchasing. In product businesses you should be able to find this info relatively easy, and especially in B2C situations.

Why is this procedure not used more often? We don’t know.

The NPS numbers that work for the calculation are those that represent a significant proportion of the overall customer experience. In e-commerce, for example, customer feedback given just after order confirmation would work. So should feedback obtained several weeks after the order was delivered.

NPS ratings from contact centers are not useful, as most customers probably never need support. (If all of your customers need to phone for help, you probably have deeper issues.)

 

Unique customer ID needed

You need to be able to match customers between your survey system and your ordering system. You must be able to see whether a particular customer has only ordered once, or multiple times. If you also have data on order value that is helpful, but not essential.

The premise of the calculation is simple: unhappy customers are less likely to order multiple times. Unless your measurement system has been biased in some way, your own results should confirm this logic right away.

 

Segment by NPS category

To make the results easy to communicate we suggest doing the calculations by NPS category. The number we are looking for is the proportion of customers who place repeat orders, broken down by Promoter, Passive and Detractor.

Here is an example we adapted from a real-world e-commerce case. The company sent the feedback request just after order confirmation. They had a 32% response rate, with 4,196 survey responses.

 

NewRepeatTotal
Promoters1,07937%1,86963%2,948
Passives32658%23842%564
Detractors27963%16737%446
Total1,6842,2743,958

To clarify, the figures mean, for example, that 2,948 customers gave a 9 or 10 rating to the “How likely are you to recommend…” question. Of these, 63% were repeat customers and 37% had ordered for the first time.

Calculate

In the real-life case, the values of repeat orders and first orders were similar. Furthermore, the value of repeat orders did not vary significantly by NPS category. Your situation may vary, and you may need to adjust your sums.

Here are the calculations:

 

LabelType of InformationValueSource*
AValue of one order100From your ordering system
BAverage orders per repeat customer per year2.2From your ordering system
CValue of moving one customer from one-time to repeat customer; that is, the value of additional orders 120(B – A) * A
DProportion of Promoters who are repeat customer 63%NPS and ordering system 
EProportion of Passives who are repeat customers 42%From table above 
FProportion of Detractors who are repeat customers 37%From table above 
GWeighted average for Passives and Detractors 40%(E + F) / 2, then rounded up 
HDifference in probability of repeat business by Promoters 23%D – G 
IValue per thousand customers moved to Promoter $27,600C * H 

Possible imperfections in the calculation

If you have particularly low response rates, say less than 10%, the results become biased. The proportions of Promoters and Detractors in your sample will be greater than that in your general customer population. This is because in a low-response situation it is those who have extreme feelings that are most likely to respond.

There is another obvious imperfection in the calculation, and it makes the results conservative. A customer who has only ordered once could be a new customer. If yours is a new company with low response rates, we suggest you explicitly assume that the low response rates and the newness of your company balance each other out.

The table above gives you a number for a period of 12 months. Hopefully your customers will stay with you for longer.

Your company may have a formal ‘qualifying period’ for ROI justifications. If, for example, you are required to have a positive ROI within 18 months, we suggest using 18 months instead of the annual number. It’s as easy as that.

We cover a more sophisticated proposal for how to calculate customer lifetime value in a separate article, How Much is a Customer Worth? Good Question.

 

Applying the calculation to contract renewals

We believe the same principles can be applied to businesses that depend on contract renewals. There are some not-so-subtle differences. In product businesses the costs of serving happy and unhappy customers tend to be similar. Not so in contract businesses, where your efforts to recover the customer may even make retaining them unprofitable.

Furthermore, in contract businesses, do not be surprised to discover that Passives are less likely to renew than Detractors. This pattern can also arise in a product business, though it is rare. The reasons behind the phenomenon may be surprising. They will be the subject of another article.

 

Conclusion

If you have a common customer ID that is shared between your feedback system and your ordering system, you may be in luck. At the very least you should be able to determine the relationship between survey responses and actual customer buying behavior.

If there is no particular relationship, your feedback system has major issues. If the relationship is as expected, you should be able to use the resulting calculations to justify improvement investments.

Over time you will build knowledge and track record. They will enable you to improve the accuracy of the predicted impact of a given CX project on real-world customer behavior.

Soon enough, you can stop tearing your hair out.

ABOUT INSIGHTS FROM OCX COGNITION

OCX Cognition delivers the future of NPS. We ensure customer experience success by combining technology and data science with programmatic consulting. In our Insights section, we present a comprehensive and evolving collection of resources based on our research and expertise, collected for CX leaders committed to delivering business outcomes.